🍴 Zomato Data Analysis

This repository contains a comprehensive Exploratory Data Analysis (EDA) on Zomato’s restaurant dataset, featuring 2,00,000+ restaurant records.
The goal is to uncover customer preferences, dining patterns, and service features that influence restaurant ratings and success.

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📊 Key Insights

• 🌆 City trends – Pune, Bangalore & Amritsar record some of the highest average ratings, while metros dominate in restaurant count.
• 🍻 Premium formats (Pubs, Lounges, Microbreweries) consistently score the best ratings.
• 🍕 Cuisines – North Indian & Fast Food dominate in availability, while Continental & Italian cuisines receive higher satisfaction levels.
• 📲 Convenience matters – Restaurants with online ordering & table booking see better ratings.
• ⭐ Customer sentiment is largely positive, with over 60% reviews rated as Good/Very Good.

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🛠️ Tools & Technologies

• Python
• Pandas
• Matplotlib
• Seaborn
• Google Colab

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📂 Repository Structure

├── data/ ├── notebooks ├── visuals ├── Zomato Data Analysis Report.pdf └── README.md

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📄 Report & Notebook

• 📕 PDF Report
• 🔗 Colab Notebook

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🚀 Future Scope

• Build predictive models for restaurant ratings.
• Perform customer segmentation using clustering.
• Explore pricing optimization based on affordability & demand.

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📜 License

This project is open-source under the MIT License.

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